For years, the commodities market has been a hard-to-access domain for investors seeking high-risk, high-reward opportunities. However, with the automation of the trading floors in 2005 and the advancements in technology, the commodities markets have recently become increasingly accessible to the individual investor.

ExitPoints.com with 90 years of combined futures trading experience has created mechanical futures trading systems to aid investors gain access to these markets. These trading systems work hand-in-hand with today’s automated trading platforms taking all human emotion out of managing money. No longer will your financial portfolio be manipulated by any one individual broker, financial planner or trader that may or may not be having a good or bad day.

All of our systems, being out of the market at the end of the trading session each day, give the investor a smaller amount of risk and return per trade than longer trading time frame systems.

 

You should also keep in mind that regardless of the consistency of the past performance that past results are not indicative of future gains. There is always risk in trading these markets, and only you can determine the appropriate amount of money to be used. However we feel that we offer a very fair risk reward ratio coupled with a wide range of diversification to an otherwise risky market. Our trading systems can complement any well-rounded portfolio.

 

The                             Strategy Overview

 

The ExitPoints.com trading systems are totally mechanical with no discretion or emotion attached to them. Our systems are not high frequency and do not try to capitalize on minor price inefficiencies in the markets. As such we do not incur a large amount of commission costs or get caught in mass triggered movements as other mechanical systems.

All of our entries are made with stop orders to enter on breakouts. The entry price is a measured amount from the previous day’s closing price. As an example of our overall trading concept, a long trade would be initiated if momentum is sufficient to reaching the entry price during the day, then the price will be much higher by the end of the day.

Our systems exits their trades in one of two methods: at the end of the session or by a stop loss order.

The Stop-Loss Exit – To limit the risk on each trade a stop loss is always placed if an entry fills. Most of the trades are not stopped out.

Near the Close Exit – We always trade the electronic markets but our exit is near the time when the pit closes or end of the trading session, if there is not a pit for that market.

Our trading systems also use several basic proprietary indicators. Volatility Breakouts, Patterns, and Momentum to name a few. If we were to use any one of these indicators individually the results would be disappointing.

Volatility Breakout – The systems use volatility breakout signals as a method to calculate trading events. The volatility is a function of the average true daily price range. After commissions and slippage this indicator system would be at best break even.

Filters: Pattern and Momentum Indicators – Our systems use these indicators to filter the raw Volatility Breakout signals mentioned above to identify profitable trading events. In other words the filtering decides if orders for a market are placed each day. With filtering the normal characteristics are that the trades are only about 50% accurate but the winners are much larger than the losers.

Finally, a trade memory is used to decide when it is best to use the indicators for filtering. Our software has populated a warehouse of unfiltered trades over several decades of price data from thirty-one different markets. This warehouse has more than 100,000 historical unfiltered day trades all using the same mechanical volatility breakout approach.

Our system then uses the warehouse to decide which trades provide highest expected return before triggering a trading event.

We are not aware of anyone else using these multi-step approach. There are three advantages to approaching the development in this manner.

• There is no optimization of parameters (curve fitting) to inflate the estimated future performance.

• There is no mathematical model to weight the indicators.

• Allows for more realistic out-of-sample testing.